DESCRIPTION: Many work physiologists recommend expressing measured oxygen uptake as a percentage of maximum oxygen uptake (VO2max), commonly known as relative workload, since it provides a subject-specific workload (Astrand and Rodahl 1986, Bonjer 1971, Rohmert 1973, Kamon 1979, Tomlinson and Manenica 1977). In addition to accounting for individual differences in physiological capacities among workers, relative workload also enables more accurate assessment of potentials of physical fatigue. Once a subject?s VO2max is known, the determination of relative workload is arithmetically simple. Determining VO2max is accomplished through the use of direct (exact) measurement or prediction techniques. Exact measurement of VO2max is impractical for applied research due to its intensive and intricate laboratory procedures as well as the risks it poses for unfit subjects. Prediction techniques offer an attractive alternative with numerous linear and non-linear regression techniques available. The specific aim of this research is to develop a practical and direct method to predict relative workload from in-situ collected sub-maximal oxygen uptake data without the need to determine maximum oxygen uptake. The method is developed by modeling the human cellular utilization system as a stochastic system and on the hypothesis that oxygen uptake data are serially dependent, and that by exploiting this dependence using time series analysis techniques, a regression model between relative workload and a statistical characteristic of collected oxygen uptake data can be developed. If successful, the technique proposed in this project will be vital in achieving one of the goals of Healthy People 2001 (U.S. Department of Health and Human Services 2000) to "promote the health and safety of people at work through prevention and early intervention." In addition, this new technique will help in better understanding the physical demands for today?s workforce doing today?s work and will have widespread application in identifying excessively demanding tasks so can be better matched to the abilities of subjects.